# For the CRAN version
install.packages("pcaone")
# For the latest developing version
devtools::install_github("Zilong-Li/PCAoneR")
This is a basic example which shows you how to use pcaone:
library(pcaone)
mat <- matrix(rnorm(100*5000), 100, 5000)
res <- pcaone(mat, k = 10)
str(res)
#> List of 3
#> $ d: num [1:10] 80.3 79.7 79.2 79.1 78.6 ...
#> $ u: num [1:100, 1:10] -0.0815 0.0835 0.0976 0.1692 -0.0568 ...
#> $ v: num [1:5000, 1:10] 0.0283 -0.01188 0.01336 0.00234 -0.01061 ...
#> - attr(*, "class")= chr "pcaone"
Let’s see the performance of pcaone
compared to the other rsvd
packages.
library(microbenchmark)
library(pcaone)
library(rsvd)
data(tiger)
timing <- microbenchmark(
'SVD' = svd(tiger, nu=150, nv=150),
'rSVD' = rsvd(tiger, k=150, q = 3),
'pcaone.alg1' = pcaone(tiger, k=150, p = 3, method = "alg1"),
'pcaone.alg2' = pcaone(tiger, k=150, p = 3, windows = 8),
times=10)
print(timing, unit='s')
#> Unit: seconds
#> expr min lq mean median uq max neval
#> SVD 6.3386527 6.4493697 6.5878084 6.4936343 6.6752989 7.2448005 10
#> rSVD 2.7598743 2.8006495 2.8523624 2.8390449 2.8630295 3.0286470 10
#> pcaone.alg1 0.5111962 0.5174421 0.5360362 0.5257972 0.5529187 0.5814665 10
#> pcaone.alg2 0.7594326 0.7668610 0.7872839 0.7833292 0.7878939 0.8441923 10
The above test is run on my MacBook Pro 2019 with processor 2.6 GHz
6-Core Intel Core i7. Note that the external BLAS or MKL routine is
disabled by
export OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 MKL_NUM_THREADS=1
.
- Zilong Li, Jonas Meisner, Anders Albrechtsen (2022). PCAone: fast and accurate out-of-core PCA framework for large scale biobank data
- Wenjian Yu, Yu Gu, Jian Li, Shenghua Liu, Yaohang Li (2017). Single-Pass PCA of Large High-Dimensional Data
- write
configure
to detect and use MKL - add
center
andscale
method